Non-RT RIC rApps for Long-Term O-RAN Interference Mitigation
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Solution Overview
Problem
Existing interference mitigation approaches in Open Radio Access Networks (O-RAN) rely on near-RT RICs, which lack the capability to detect long-term interference patterns and suffer from concept drift, leading to suboptimal interference detection and avoidance due to reliance on transient data and manual threshold updates.
Innovation Solution
Utilizing rApps on non-RT RICs to provide long-term pattern analysis and dynamic threshold updates for interference detection, and recommending slot bans based on continuous quality improvement scores to enhance interference mitigation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If near-RT RICs are used for interference mitigation, then real-time interference detection and avoidance can be achieved, but the system cannot detect long-term interference patterns and suffers from concept drift
Solution Approach 1:
The interference mitigation system is segmented into two distinct components: near-RT RICs for real-time interference detection and avoidance, and non-RT RICs for long-term pattern analysis and threshold updates. This segmentation allows each component to specialize in its strength while working together to overcome individual limitations.
Solution Approach 2:
The non-RT RIC acts as an intermediary that provides long-term traffic patterns and dynamic threshold updates to the near-RT RIC. This intermediary relationship enables the near-RT RIC to focus on real-time responses while benefiting from the analytical capabilities of the non-RT RIC.
2Device complexity
If manual threshold updates are used in near-RT RICs, then simple implementation is achieved, but interference detection becomes suboptimal due to concept drift
Solution Approach 1:
The non-RT RIC automatically performs long-term pattern analysis and generates dynamic threshold updates without manual intervention. This self-service capability eliminates concept drift by continuously adapting thresholds based on evolving traffic patterns, while the near-RT RIC simply applies these updates.
Solution Approach 2:
The system implements a feedback loop where the non-RT RIC continuously monitors traffic patterns, identifies long-term interference patterns, and updates thresholds accordingly. This feedback mechanism ensures that interference detection precision is maintained despite changing network conditions.
3Loss of time
If transient data is used for interference detection, then fast processing is achieved, but detection accuracy deteriorates due to lack of long-term pattern information
Solution Approach 1:
The non-RT RIC performs preliminary analysis of long-term traffic patterns and pre-calculates optimal thresholds before the near-RT RIC needs to make real-time decisions. This preliminary action allows the near-RT RIC to use fast transient data processing while benefiting from pre-computed long-term insights.
Data Source
AI summary
Architectures and techniques are described that can provide interference mitigation techniques by leveraging rApps the execute on a non-real time radio access network intelligent controller (non-RT RIC). One or more rApps can operate to assist in interference detection as well as interference avoidance. The interference detection rApp can determine a recommended threshold based on longer term trends or patterns, which can be used by an associated xApp executing on a near-real time radio access network intelligent controller (near-RT RIC) that typically lacks such capability. The interference avoidance rApp can determine a recommended slot to ban based on longer term trends or patterns, which can be used by an associated xApp or scheduler. For instance, the scheduler can use the slot banning recommendation from either the rApp or the xApp.